ThisbookisallaboutDTrace,withtheemphasisonusingDTracetounderstand,observe,anddiagnosesystemsandapplications.AdeepunderstandingofthedetailsofhowDTraceworksisnotnecessarytousingDTracetodiagnoseandsolveproblems;thus,thebookcoversusingDTraceonsystemsandapplications,withcommand-lineexamplesandagreatmanyDscripts.Dependingonyourlevelofexperience,weintendthebook’sorganizationtofacilitateitsuseasareferenceguide,allowingyoutorefertospecificchapterswhendiagnosingaparticularareaofthesystemorapplication.

EclipseMaven创建Web项目报错Couldnotresolvearchetypeorg.apache.maven.archetypes:maven-archetype-webap

相信社区中很多小伙伴和我一样使用了很长时间的Caffe深度学习框架，也非常希望从代码层次理解Caffe的实现从而实现新功能的定制。
本文将从整体架构和底层实现的视角，对Caffe源码进行解析。
Caffe框架主要有五个组件，Blob，Solver，Net，Layer，Proto，其结构图如下图1所示。
Solver负责深度网络的训练，每个Solver中包含一个训练网络对象和一个测试网络对象。
每个网络则由若干个Layer构成。
每个Layer的输入和输出Featuremap表示为InputBlob和OutputBlob。
Blob是Caffe实际存储数据的结构，是一个不定维的矩阵，在Caffe中一般用来表

gflags是google的一个开源的处理命令行参数的库，是ceressolver必不可少的库，2.0版本是一个带有sln文件的版本，不用自己cmake就可以使用，很方便。

Title:RMachineLearningEssentialsAuthor:MicheleUsuelliLength:218pagesEdition:1Language:EnglishPublisher:PacktPublishingPublicationDate:2014-11-25ISBN-10:178398774XISBN-13:9781783987740GainquickaccesstothemachinelearningconceptsandpracticalapplicationsusingtheRdevelopmentenvironmentAboutThisBookBuildmachinelearningalgorithmsusingthemostpowerfultoolsinRIdentifybusinessproblemsandsolvethembydevelopingeffectivesolutionsHands-ontutorialexplainingtheconceptsthroughlotsofpracticalexamples,tipsandtricksWhoThisBookIsForIfyouwanttolearnhowtodevelopeffectivemachinelearningsolutionstoyourbusinessproblemsinR,thisbookisforyou.Itwouldbehelpfultohaveabitoffamiliaritywithbasicobject-orientedprogrammingconcepts,butnopriorexperienceisrequired.InDetailRMachineLearningEssentialsprovidesyouwithanintroductiontomachinelearningwithR.Machinelearningfindsitsapplicationsinspeechrecognition,search-basedoperations,andartificialintelligence,amongotherthings.Youwillstartoffbygettinganintroductiontowhatmachinelearningis,alongwithsomeexamplestodemonstratetheimportanceinunderstandingthebasicideasofmachinelearning.ThisbookwillthenintroduceyoutoRandyouwillseethatitisaninfluentialprogramminglanguagethataidseffectivemachinelearning.Youwilllearnthethreestepstobuildaneffectivemachinelearningsolution,whichareexploringthedata,buildingthesolution,andvalidatingtheresults.Thebookwilldemonstrateeachstep,highlightingtheirpurposeandexplainingtechniquesrelatedtothem.Bytheendofthisbook,youwillbeabletousethemachinelearningtechniqueseffectively,identifybusinessproblems,andsolvethembyapplyingappropriatesolutions.TableofContentsChapter1.TransformingDataintoActionsChapter2.R–APowerfulToolforDevelopingMachineLearningAlgorith

DEA软件包，包括DEAExcelSolver、DEAP2.1、mydea各种版本及说明

//1.Asmall-setinsertionsort.Wedothisonanysetwith＜=32elements//2.Apartitioningkernel,which-givenapivot-separatesaninput//arrayintoelementspivot.Twoquicksortswillthen//belaunchedtoresolveeachofthese.//3.Aquicksortco-ordinator,whichfiguresoutwhatkernelstolaunch//andwhen.

Thispracticalguideprovidesnearly200self-containedrecipestohelpyousolvemachinelearningchallengesyoumayencounterinyourdailywork.Ifyou’recomfortablewithPythonanditslibraries,includingpandasandscikit-learn,you’llbeabletoaddressspecificproblemssuchasloadingdata,handlingtextornumericaldata,modelselection,anddimensionalityreductionandmanyothertopics.Eachrecipeincludescodethatyoucancopyandpasteintoatoydatasettoensurethatitactuallyworks.Fromthere,youcaninsert,combine,oradaptthecodetohelpconstructyourapplication.Recipesalsoincludeadiscussionthatexplainsthesolutionandprovidesmeaningfulcontext.Thiscookbooktakesyoubeyondtheoryandconceptsbyprovidingthenutsandboltsyouneedtoconstructworkingmachinelearningapplications.You’llfindrecipesfor:Vectors,matrices,andarraysHandlingnumericalandcategoricaldata,text,images,anddatesandtimesDimensionalityreductionusingfeatureextractionorfeatureselectionModelevaluationandselectionLinearandlogicalregression,treesandforests,andk-nearestneighborsSupportvectormachines(SVM),naïveBayes,clustering,andneuralnetworksSavingandloadingtrainedmodels

支持向量机源码，可在www.csie.ntu.edu.tw/~cjlin/libsvm/下载到最新版本，该版本是2013年4月更新的，3.17版。
压缩包里面有源代码和文档。
以下摘自前述网站：IntroductionLIBSVMisanintegratedsoftwareforsupportvectorclassification,(C-SVC,nu-SVC),regression(epsilon-SVR,nu-SVR)anddistributionestimation(one-classSVM).Itsupportsmulti-classclassification.Sinceversion2.8,itimplementsanSMO-typealgorithmproposedinthispaper:R.-E.Fan,P.-H.Chen,andC.-J.Lin.WorkingsetselectionusingsecondorderinformationfortrainingSVM.JournalofMachineLearningResearch6,1889-1918,2005.Youcanalsofindapseudocodethere.(howtociteLIBSVM)OurgoalistohelpusersfromotherfieldstoeasilyuseSVMasatool.LIBSVMprovidesasimpleinterfacewhereuserscaneasilylinkitwiththeirownprograms.MainfeaturesofLIBSVMincludeDifferentSVMformulationsEfficientmulti-classclassificationCrossvalidationformodelselectionProbabilityestimatesVariouskernels(includingprecomputedkernelmatrix)WeightedSVMforunbalanceddataBothC++andJavasourcesGUIdemonstratingSVMclassificationandregressionPython,R,MATLAB,Perl,Ruby,Weka,CommonLISP,CLISP,Haskell,OCaml,LabVIEW,andPHPinterfaces.C#.NETcodeandCUDAextensionisavailable.It'salsoincludedinsomedataminingenvironments:RapidMiner,PCP,andLIONsolver.Automaticmodelselectionwhichcangeneratecontourofcrossvaliationaccuracy.

MIMOOFDMSimulator:OFDM.m:OFDMSimulator(outerfunction)create_channel.m:GeneratesaRayleighfadingfrequency-selectivechannel,parametrizedbytheantennaconfiguration,theOFDMconfiguration,andthepower-delayprofile.svd_decompose_channel.m:Sincefullchannelknowledgeisassumed,transmissionisacrossparallelsingularvaluemodes.Thisfunctiondecomposesthechannelintothesemodes.BitLoad.m:Applythebit-loadingalgorithmtoachievethedesiredbitandenergyallocationforthecurrentchannelinstance.ComputeSNR.m:Giventhesubcarriergains,thissimplefunctiongeneratestheSNRvaluesofeachchannel(eachsingularvalueoneachtoneisaseparatechannel).chow_algo.m:ApplyChow'salgorithmtogenerateaparticularbitandenergyallocation.EnergyTableInit.m:GiventheSNRvalues,formatableofenergyincrementsforeachchannel.campello_algo.m:ApplyCampello'salgorithmtoconvergetotheoptimalbitandenergyallocationforthegivenchannelconditions.ResolvetheLastBit.m:Anoptimalbit-loadingofthelastbitrequiresauniqueoptimization.modulate.m:Modulatetherandominputsequenceaccordingtothebitallocationsforeachchannel.ENC2.mat:BPSKModulatorENC4.mat:4-QAMModulator(Graycoded)ENC16.mat:16-QAMModulator(Graycoded)ENC64.mat:64-QAMModulator(Graycoded)ENC256.mat:256-QAMModulator(Graycoded)precode.m:Precodethetransmittedvectorateachtimeinstancebyfilteringthemodulatedvectorwiththeright-inverseofthechannel'srightsingluarmatrix.ifft_cp_tx_blk.m:IFFTblockoftheOFDMsystem.channel.m:ApplythechanneltotheOFDMframe.fft_cp_rx_blk.m:FFTblockoftheOFDMsystem.shape.m:Completethediagonalizationofthechannelbyfilteringthereceivedvectorwiththeleft-inverseofthechannel'sleftsingularmatrix.demodulate.m:Performanearestneighborsearchknowingthetransmitconstellationused.

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。